{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, glob\n",
    "import numpy as np \n",
    "import torch \n",
    "from medpy.io import load, header\n",
    "from typing import Tuple\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import nrrd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = r\"D:\\miccai2023\\data\\radiology\\cc18-HN015\"\n",
    "tumor_label, _ = load(os.path.join(\n",
    "    path, \"label.nii.gz\"))\n",
    "ct_image, _ = load(os.path.join(\n",
    "    path, \"CT_img.nii.gz\"))\n",
    "lymph_node, header = nrrd.read(os.path.join(\n",
    "    path, \"Segmentation.nrrd\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "from medpy.io import load, header\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import h5py\n",
    "import pickle\n",
    "import math\n",
    "from parameters import mkdirs\n",
    "import torch\n",
    "import nrrd\n",
    "import os\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "OPENSLIDE_PATH = r'C:\\Users\\amaury.leroy\\openslide-win64-20221217\\openslide-win64-20221217\\bin'\n",
    "\n",
    "if hasattr(os, 'add_dll_directory'):\n",
    "    # Python >= 3.8 on Windows\n",
    "    with os.add_dll_directory(OPENSLIDE_PATH):\n",
    "        import openslide\n",
    "else:\n",
    "    import openslide\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(r'C:\\Users\\bsong47\\Documents\\code\\raptomics\\checkpoints\\raw_images\\fused_attention_multitask\\fused_attention_multitask_results_auc.pkl', 'rb') as f:\n",
    "    data = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "results = torch.load(r'C:\\Users\\bsong47\\Documents\\code\\raptomics\\checkpoints\\raw_images\\fused_attention_multitask\\fused_attention_multitask.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5218655509408563,\n",
       " 0.34201654946804044,\n",
       " 0.3381701748371124,\n",
       " 0.3345834389925003,\n",
       " 0.3296836284995079,\n",
       " 0.3425289306938648,\n",
       " 0.3536672833114862,\n",
       " 0.3279810672402382,\n",
       " 0.33863659179210664,\n",
       " 0.33579184436798093,\n",
       " 0.3308468781709671,\n",
       " 0.3300914249420166,\n",
       " 0.3245387782454491,\n",
       " 0.3285018632411957,\n",
       " 0.3373358391523361,\n",
       " 0.3180977246463299,\n",
       " 0.321259016007185,\n",
       " 0.34247979105558213,\n",
       " 0.3376782059669495,\n",
       " 0.3395612097978592,\n",
       " 0.3359736642688513,\n",
       " 0.3304595009088516,\n",
       " 0.33252699778974054,\n",
       " 0.325010457277298,\n",
       " 0.3295879908800125,\n",
       " 0.32397964149713515,\n",
       " 0.3284494436979294,\n",
       " 0.3260821542739868,\n",
       " 0.3214443693757057,\n",
       " 0.3196244342327118]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['metrics']['train']['loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15c27066ca0>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(results['metrics']['val']['grad_auc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # #correlation heatmap\n",
    "    # # Step 1: Read CSV file into DataFrame\n",
    "    # file_path = r'C:\\Users\\bsong47\\Downloads\\data_table_test_penultimate.csv'\n",
    "    # data = pd.read_csv(file_path)\n",
    "\n",
    "    # # Select columns of interest\n",
    "    # columns_of_interest = [\"path0\", \"path1\", \"path2\", \"path3\", \"path4\", \"path5\", \"path6\",\n",
    "    #                     \"pathology_1\", \"pathology_2\", \"pathology_3\", \"pathology_4\", \n",
    "    #                     \"pathology_5\", \"pathology_6\", \"pathology_7\", \"pathology_8\", \n",
    "    #                     \"pathology_9\", \"pathology_10\", \"pathology_11\", \"pathology_12\", \n",
    "    #                     \"pathology_13\", \"pathology_14\", \"pathology_15\", \"pathology_16\"]\n",
    "\n",
    "    # # Subset the data to include only the columns of interest\n",
    "    # data_subset = data[columns_of_interest]\n",
    "\n",
    "    # # Step 2: Compute correlation or covariance matrix\n",
    "    # # Uncomment one of the following lines based on whether you want correlation or covariance\n",
    "    # correlation_matrix = data_subset.corr()  # Compute correlation matrix\n",
    "    # # covariance_matrix = data_subset.cov()  # Compute covariance matrix\n",
    "\n",
    "    # # Step 3: Visualize in a heatmap\n",
    "    # plt.figure(figsize=(12, 10))  # Adjust the figure size as needed\n",
    "\n",
    "    # import seaborn as sns\n",
    "    # # Uncomment one of the following lines based on whether you want to plot correlation or covariance\n",
    "    # sns.heatmap(correlation_matrix, cmap='coolwarm', cbar=True, square=True, annot=False)\n",
    "    \n",
    "    # # sns.heatmap(covariance_matrix, annot=True, cmap='coolwarm')\n",
    "\n",
    "    # # Add title and adjust font sizes\n",
    "    # plt.xticks(fontsize=10)\n",
    "    # plt.yticks(fontsize=10)\n",
    "\n",
    "    # # Add colorbar\n",
    "    # # plt.colorbar()\n",
    "\n",
    "    # # Show plot\n",
    "    # plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # # scatter plot of HR and p values\n",
    "    # # Step 1: Read CSV file into DataFrame\n",
    "    # data = pd.read_csv(r'C:\\Users\\bsong47\\Downloads\\hazard_ratios_and_p_values_OS.csv')\n",
    "\n",
    "    # # Step 2: Data Processing\n",
    "    # # Convert columns to numeric\n",
    "    # data['hazard_ratios'] = pd.to_numeric(data['hazard_ratios'], errors='coerce')\n",
    "    # data['p_values'] = pd.to_numeric(data['p_values'], errors='coerce')\n",
    "\n",
    "    # # Drop rows with NaN values\n",
    "    # data = data.dropna()\n",
    "\n",
    "    # # Compute logarithms\n",
    "    # data['log_hazard_ratios'] = np.log(data['hazard_ratios'])\n",
    "    # data['log_p_values'] = -np.log(data['p_values'])\n",
    "\n",
    "    # # Step 3: Create a scatter plot with legend\n",
    "    # plt.figure(figsize=(10, 8))\n",
    "\n",
    "    # # Define categories and corresponding colors using 'jet' colormap\n",
    "    # categories = data['category'].unique()\n",
    "    # num_categories = len(categories)\n",
    "    # colors = plt.cm.jet(np.linspace(0, 0.9, num_categories))\n",
    "\n",
    "    # # Plot each category separately to create legend entries\n",
    "    # for i, category in enumerate(categories):\n",
    "    #     subset = data[data['category'] == category]\n",
    "    #     plt.scatter(subset['log_hazard_ratios'], subset['log_p_values'], color=colors[i], label=category, alpha=1, edgecolors='black', linewidth=1)\n",
    "\n",
    "    # # Add labels and title with bold font\n",
    "    # plt.xlabel('log(hazard ratios)', fontsize=14, fontweight='bold')\n",
    "    # plt.ylabel('-log(p values)', fontsize=14, fontweight='bold')\n",
    "\n",
    "    \n",
    "    # # Add horizontal dashed line at y = 2.9957\n",
    "    # plt.axhline(y=2.9957, color='gray', linestyle='--')\n",
    "    # legend_properties = {'weight':'bold'}\n",
    "\n",
    "    # plt.legend(prop=legend_properties)\n",
    "    # # Add legend with 'best' location and adjust font properties\n",
    "    # # legend = plt.legend(loc='best', fontsize=12, title='Category', title_fontsize='13',fontweight='bold')\n",
    "\n",
    "    # # Save figure at 500 dpi resolution\n",
    "    # plt.savefig('scatter_plot_hazard_p_values.png', dpi=500)\n",
    "\n",
    "    # # Show the plot (optional)\n",
    "    # plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # # T-SNE plot of penultimate features\n",
    "    # file_path = r'C:\\Users\\bsong47\\OneDrive - Emory University\\Documents\\MATLAB\\penultimate_raptomic.csv'\n",
    "    # df = pd.read_csv(file_path)\n",
    "\n",
    "    # # Step 2: Exclude the first column from feature columns\n",
    "    # feature_columns = [col for col in df.columns if col.startswith('test_') and col != 'test_ID_cell']\n",
    "    # quantitative_features = df[feature_columns].values\n",
    "\n",
    "    # # Step 3: Exclude the first column from labels\n",
    "    # labels = df[['dfs_3year', 'site']]\n",
    "\n",
    "    # # Step 4: Map 'dfs_3year' categorical values to numeric values\n",
    "    # dfs_3year_mapping = {'Event': 0, 'Censored': 1}\n",
    "    # labels['dfs_3year'] = labels['dfs_3year'].map(dfs_3year_mapping)\n",
    "\n",
    "    # # Step 5: Map 'site' categorical values to numeric values\n",
    "    # site_mapping = {'CCF': 0, 'Winship': 1}  # Replace 'OtherSite' with the actual category name\n",
    "    # labels['site'] = labels['site'].map(site_mapping)\n",
    "\n",
    "    # # Step 6: Perform t-SNE dimensionality reduction\n",
    "    # tsne = TSNE(n_components=2, random_state=10, verbose=5, perplexity=10, n_iter=300)\n",
    "    # tsne_embeddings = tsne.fit_transform(quantitative_features)\n",
    "\n",
    "    # # Step 7: Plot t-SNE embeddings with labels colored accordingly\n",
    "    # plt.figure(figsize=(10, 5))\n",
    "\n",
    "    # # Plot for Survival outcomes\n",
    "    # plt.subplot(1, 2, 1)\n",
    "    # scatter2 = plt.scatter(tsne_embeddings[:, 0], tsne_embeddings[:, 1], c=labels['site'], cmap='viridis')\n",
    "    # plt.title('t-SNE - Sites')\n",
    "    # plt.legend(handles=scatter2.legend_elements()[0], labels=site_mapping.keys(), loc='best')\n",
    "\n",
    "    # # Plot for Sites\n",
    "    # plt.subplot(1, 2, 2)\n",
    "    # scatter1 = plt.scatter(tsne_embeddings[:, 0], tsne_embeddings[:, 1], c=labels['dfs_3year'], cmap='viridis')\n",
    "    # plt.title('t-SNE - 3-year DFS')\n",
    "    # plt.legend(handles=scatter1.legend_elements()[0], labels=['Event', 'Censored'], loc='best')\n",
    "\n",
    "    # plt.tight_layout()\n",
    "    # # Save the figure\n",
    "    # save_path = r'C:\\Users\\bsong47\\OneDrive - Emory University\\Documents\\Raptomic_OPSCC_paper\\tsne_plots.jpg'\n",
    "    # plt.savefig(save_path, dpi=600)\n",
    "    # plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "# Load the pickle file\n",
    "pickle_path = r\"C:\\Users\\bsong47\\OneDrive - Emory University\\Documents\\code\\raptomics\\checkpoints\\raw_images\\fused_attention_multitask_100_0.002_raptomic\\fused_attention_multitask_100_0.002_raptomic_pred_test.pkl\"\n",
    "with open(pickle_path, 'rb') as file:\n",
    "    data = pickle.load(file)\n",
    "\n",
    "# Extract prediction scores and ground truth\n",
    "pred_scores = data[0]  # First element: prediction scores\n",
    "ground_truth = data[2]  # Third element: binary ground truth\n",
    "\n",
    "# If they are tensors, move them to the CPU, detach from graph, and convert to NumPy arrays\n",
    "if hasattr(pred_scores, 'cpu'):\n",
    "    pred_scores = pred_scores.detach().cpu().numpy()\n",
    "\n",
    "if hasattr(ground_truth, 'cpu'):\n",
    "    ground_truth = ground_truth.detach().cpu().numpy()\n",
    "\n",
    "# Compute ROC curve and AUC\n",
    "fpr, tpr, thresholds = roc_curve(ground_truth, pred_scores)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve with adjusted size and 300 dpi resolution\n",
    "plt.figure(figsize=(6, 6), dpi=300)\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=4, label=f'AUC = {roc_auc:.2f}')  # Bold line (lw=3)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=3, linestyle='--')  # Reference line\n",
    "\n",
    "# Increase font sizes for axes and legend, set Arial font\n",
    "plt.xlabel('False Positive Rate', fontname='Arial', fontsize=16)  # Larger font for x-axis\n",
    "plt.ylabel('True Positive Rate', fontname='Arial', fontsize=16)  # Larger font for y-axis\n",
    "plt.xticks(fontname='Arial', fontsize=14)\n",
    "plt.yticks(fontname='Arial', fontsize=14)\n",
    "\n",
    "# Increase legend font size and set Arial font explicitly\n",
    "legend = plt.legend(loc=\"lower right\", prop={'family': 'Arial', 'size': 21})\n",
    "\n",
    "# Save the figure to the specified path with 300 dpi\n",
    "save_path = r\"C:\\Users\\bsong47\\OneDrive - Emory University\\Documents\\Raptomic_OPSCC_paper\\CancerDiscovery\\figures\\roc_curve_test.png\"\n",
    "plt.savefig(save_path, dpi=300)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Age  Smoking PY     SMuRF  T-stage  N-stage  Sex  Treatment     DFS  \\\n",
      "0     78        50.0  0.983866        3        1    1          1    6.00   \n",
      "1     51        30.0  0.974916        3        2    1          1    5.00   \n",
      "2     82        10.0  0.956352        3        2    1          1   10.00   \n",
      "3     64        18.0  0.938854        1        2    1          1    3.00   \n",
      "4     69         0.0  0.990411        1        1    1          1   47.00   \n",
      "..   ...         ...       ...      ...      ...  ...        ...     ...   \n",
      "152   61        80.0  0.289839        1        0    1          0   81.32   \n",
      "153   64         0.0  0.809112        0        1    1          1   47.53   \n",
      "154   49         0.0  0.608913        0        2    1          1  110.23   \n",
      "155   59         0.0  0.561308        1        2    1          1  118.82   \n",
      "156   55        20.0  0.393863        1        2    1          1   58.88   \n",
      "\n",
      "     DFS_censor  \n",
      "0             1  \n",
      "1             1  \n",
      "2             1  \n",
      "3             1  \n",
      "4             0  \n",
      "..          ...  \n",
      "152           0  \n",
      "153           0  \n",
      "154           0  \n",
      "155           0  \n",
      "156           0  \n",
      "\n",
      "[157 rows x 9 columns]\n",
      "                coef  exp(coef)  se(coef)  coef lower 95%  coef upper 95%  \\\n",
      "covariate                                                                   \n",
      "Age         0.017710   1.017867  0.019739       -0.020978        0.056397   \n",
      "Smoking PY  0.022050   1.022295  0.008676        0.005045        0.039056   \n",
      "SMuRF       2.767136  15.912988  0.567311        1.655226        3.879045   \n",
      "T-stage     0.208080   1.231311  0.177344       -0.139508        0.555667   \n",
      "N-stage     0.779924   2.181306  0.319746        0.153232        1.406615   \n",
      "Sex         0.144142   1.155049  0.766717       -1.358594        1.646879   \n",
      "Treatment  -1.141291   0.319407  0.604194       -2.325489        0.042908   \n",
      "\n",
      "            exp(coef) lower 95%  exp(coef) upper 95%  cmp to         z  \\\n",
      "covariate                                                                \n",
      "Age                    0.979241             1.058017     0.0  0.897198   \n",
      "Smoking PY             1.005057             1.039828     0.0  2.541387   \n",
      "SMuRF                  5.234264            48.377999     0.0  4.877633   \n",
      "T-stage                0.869786             1.743104     0.0  1.173312   \n",
      "N-stage                1.165596             4.082115     0.0  2.439195   \n",
      "Sex                    0.257022             5.190756     0.0  0.188000   \n",
      "Treatment              0.097736             1.043842     0.0 -1.888946   \n",
      "\n",
      "                   p   -log2(p)  \n",
      "covariate                        \n",
      "Age         0.369613   1.435912  \n",
      "Smoking PY  0.011041   6.500938  \n",
      "SMuRF       0.000001  19.829025  \n",
      "T-stage     0.240671   2.054867  \n",
      "N-stage     0.014720   6.086077  \n",
      "Sex         0.850877   0.232978  \n",
      "Treatment   0.058899   4.085613  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import shap\n",
    "from lifelines import CoxPHFitter\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the data\n",
    "file_path = r\"C:\\Users\\bsong47\\OneDrive - Emory University\\Documents\\Raptomic_OPSCC_paper\\CancerDiscovery\\data_table_combined_test.csv\"\n",
    "data = pd.read_csv(file_path)\n",
    "\n",
    "# Prepare the data\n",
    "# Encode categorical variables\n",
    "categorical_columns = ['T-stage', 'N-stage', 'Sex', 'Treatment']\n",
    "label_encoders = {}\n",
    "for col in categorical_columns:\n",
    "    le = LabelEncoder()\n",
    "    data[col] = le.fit_transform(data[col])\n",
    "    label_encoders[col] = le\n",
    "\n",
    "# Define the feature set and target variable\n",
    "X = data[['Age', 'Smoking PY', 'SMuRF'] + categorical_columns]\n",
    "y = data[['DFS', 'DFS_censor']]\n",
    "combined_data = pd.concat([X, y], axis=1)\n",
    "# Fit the Cox proportional hazards model\n",
    "cph = CoxPHFitter()\n",
    "cph.fit(combined_data, duration_col='DFS', event_col='DFS_censor')\n",
    "print(combined_data)\n",
    "\n",
    "# Display the summary of the Cox model\n",
    "print(cph.summary)\n",
    "\n",
    "# Calculate SHAP values\n",
    "explainer = shap.Explainer(cph.predict_partial_hazard, X)\n",
    "shap_values = explainer(X)\n",
    "\n",
    "# Set the font to Arial and make it bold\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "plt.rcParams['font.weight'] = 'bold'  # Set font weight to bold\n",
    "\n",
    "# Plot the SHAP values using a beeswarm plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "shap.summary_plot(shap_values, X, plot_type=\"dot\", show=False)  # Prevents auto display\n",
    "\n",
    "# Set the title with bold font\n",
    "# plt.title('Beeswarm Plot of SHAP Values in Multivariate Cox Analysis (DFS)', fontsize=16, fontweight='bold')\n",
    "\n",
    "# Save the plot\n",
    "output_path = r'C:\\Users\\bsong47\\OneDrive - Emory University\\Documents\\Raptomic_OPSCC_paper\\CancerDiscovery\\figures\\beeswarm.png'\n",
    "plt.savefig(output_path, dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.close()  # Close the figure after saving\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "# Calculate mean absolute SHAP values for each feature\n",
    "mean_shap_values = np.abs(shap_values.values).mean(axis=0)\n",
    "\n",
    "# Create a DataFrame to hold feature names and their mean SHAP values\n",
    "features = X.columns\n",
    "mean_shap_df = pd.DataFrame({'Feature': features, 'Mean SHAP Value': mean_shap_values})\n",
    "\n",
    "# Sort by Mean SHAP Value\n",
    "mean_shap_df.sort_values(by='Mean SHAP Value', ascending=False, inplace=True)\n",
    "\n",
    "# Normalize mean SHAP values for color mapping with the 'Blues' colormap\n",
    "norm = plt.Normalize(mean_shap_df['Mean SHAP Value'].min(), mean_shap_df['Mean SHAP Value'].max())\n",
    "colors = cm.Blues(norm(mean_shap_df['Mean SHAP Value']))  # Use the 'Blues' colormap\n",
    "\n",
    "# Set the font to Arial and bold\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "plt.rcParams['font.weight'] = 'bold'\n",
    "\n",
    "# Custom autopct function to change the color of the proportion value for SMuRF\n",
    "def custom_autopct(pct, labels):\n",
    "    # Match label to \"SMuRF\" and set it to white\n",
    "    label = labels[custom_autopct.counter]\n",
    "    custom_autopct.counter += 1\n",
    "    if label == 'smurf':\n",
    "        return f'{pct:.1f}%' if pct > 0 else ''\n",
    "    else:\n",
    "        return f'{pct:.1f}%' if pct > 0 else ''\n",
    "\n",
    "# Initialize a counter for custom_autopct\n",
    "custom_autopct.counter = 0\n",
    "\n",
    "# Plot a pie chart\n",
    "plt.figure(figsize=(8, 8))\n",
    "wedges, texts, autotexts = plt.pie(mean_shap_df['Mean SHAP Value'], labels=mean_shap_df['Feature'], \n",
    "                                   autopct=lambda p: custom_autopct(p, mean_shap_df['Feature']),\n",
    "                                   startangle=140, colors=colors, textprops={'fontsize': 20})\n",
    "\n",
    "# Set the SMuRF variable's percentage text to white\n",
    "for i, autotext in enumerate(autotexts):\n",
    "    if mean_shap_df['Feature'].iloc[i] == 'SMuRF':\n",
    "        autotext.set_color('white')  # Set SMuRF proportion to white\n",
    "    else:\n",
    "        autotext.set_color('black')  # All other proportions remain black\n",
    "\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "# Save the pie chart\n",
    "output_path = r'C:\\Users\\bsong47\\OneDrive - Emory University\\Documents\\Raptomic_OPSCC_paper\\CancerDiscovery\\figures\\shap_mean_pie_chart.png'\n",
    "plt.savefig(output_path, dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.show()  # Display the pie chart\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.95      0.82        99\n",
      "           1       0.81      0.36      0.50        58\n",
      "\n",
      "    accuracy                           0.73       157\n",
      "   macro avg       0.76      0.66      0.66       157\n",
      "weighted avg       0.75      0.73      0.70       157\n",
      "\n",
      "[[94  5]\n",
      " [37 21]]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import shap\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the data\n",
    "file_path = r\"C:\\Users\\bsong47\\OneDrive - Emory University\\Documents\\Raptomic_OPSCC_paper\\CancerDiscovery\\data_table_combined_test.csv\"\n",
    "data = pd.read_csv(file_path)\n",
    "\n",
    "# Prepare the data\n",
    "# Encode categorical variables\n",
    "categorical_columns = ['T-stage', 'N-stage', 'Sex', 'Treatment']\n",
    "label_encoders = {}\n",
    "for col in categorical_columns:\n",
    "    le = LabelEncoder()\n",
    "    data[col] = le.fit_transform(data[col])\n",
    "    label_encoders[col] = le\n",
    "\n",
    "# Define the feature set and target variable\n",
    "X = data[['Age', 'Smoking PY', 'SMuRF'] + categorical_columns]\n",
    "y = data['grade']  # 'grade' is the target variable (endpoint)\n",
    "\n",
    "# Initialize and fit a logistic regression model\n",
    "model = LogisticRegression(max_iter=1000, random_state=42)\n",
    "model.fit(X, y)\n",
    "\n",
    "# Predict the target and display a classification report\n",
    "y_pred = model.predict(X)\n",
    "print(classification_report(y, y_pred))\n",
    "\n",
    "# Display the confusion matrix\n",
    "print(confusion_matrix(y, y_pred))\n",
    "\n",
    "# Calculate SHAP values for the logistic regression model\n",
    "explainer = shap.Explainer(model, X)\n",
    "shap_values = explainer(X)\n",
    "\n",
    "# Set the font to Arial and bold\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "plt.rcParams['font.weight'] = 'bold'\n",
    "\n",
    "# Plot the SHAP values using a beeswarm plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "shap.summary_plot(shap_values, X, plot_type=\"dot\", show=False)\n",
    "\n",
    "# Save the plot\n",
    "# output_path = r'C:\\Users\\bsong47\\OneDrive - Emory University\\Documents\\Raptomic_OPSCC_paper\\CancerDiscovery\\figures\\logistic_shap_beeswarm.png'\n",
    "plt.savefig(output_path, dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.close()  # Close the figure after saving\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bsong47\\AppData\\Local\\Temp\\ipykernel_24728\\1053373064.py:17: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n",
      "  cmap = cm.get_cmap('Blues', len(mean_shap_df))  # Create a colormap with distinct shades for each feature\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "# Calculate mean absolute SHAP values for each feature\n",
    "mean_shap_values = np.abs(shap_values.values).mean(axis=0)\n",
    "\n",
    "# Create a DataFrame to hold feature names and their mean SHAP values\n",
    "features = X.columns\n",
    "mean_shap_df = pd.DataFrame({'Feature': features, 'Mean SHAP Value': mean_shap_values})\n",
    "\n",
    "# Sort by Mean SHAP Value\n",
    "mean_shap_df.sort_values(by='Mean SHAP Value', ascending=False, inplace=True)\n",
    "\n",
    "# Generate distinct colors from the 'Blues' colormap for each feature, and reverse the color order\n",
    "cmap = cm.get_cmap('Blues', len(mean_shap_df))  # Create a colormap with distinct shades for each feature\n",
    "colors = [cmap(i) for i in range(len(mean_shap_df))][::-1]  # Reverse the order of colors\n",
    "\n",
    "# Set the font to Arial and bold\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "plt.rcParams['font.weight'] = 'bold'\n",
    "\n",
    "# Custom autopct function to change the color of the proportion value for SMuRF\n",
    "def custom_autopct(pct, labels):\n",
    "    # Match label to \"SMuRF\" and set it to white\n",
    "    label = labels[custom_autopct.counter]\n",
    "    custom_autopct.counter += 1\n",
    "    if label == 'SMuRF':\n",
    "        return f'{pct:.1f}%' if pct > 0 else ''\n",
    "    else:\n",
    "        return f'{pct:.1f}%' if pct > 0 else ''\n",
    "\n",
    "# Initialize a counter for custom_autopct\n",
    "custom_autopct.counter = 0\n",
    "\n",
    "# Plot a pie chart\n",
    "plt.figure(figsize=(8, 8))\n",
    "wedges, texts, autotexts = plt.pie(mean_shap_df['Mean SHAP Value'], labels=mean_shap_df['Feature'], \n",
    "                                   autopct=lambda p: custom_autopct(p, mean_shap_df['Feature']),\n",
    "                                   startangle=140, colors=colors, textprops={'fontsize': 20})\n",
    "\n",
    "# Set the SMuRF variable's percentage text to white\n",
    "for i, autotext in enumerate(autotexts):\n",
    "    if mean_shap_df['Feature'].iloc[i] == 'SMuRF':\n",
    "        autotext.set_color('white')  # Set SMuRF proportion to white\n",
    "    else:\n",
    "        autotext.set_color('black')  # All other proportions remain black\n",
    "\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "# Save the pie chart\n",
    "output_path = r'C:\\Users\\bsong47\\OneDrive - Emory University\\Documents\\Raptomic_OPSCC_paper\\CancerDiscovery\\figures\\shap_mean_pie_chart_grade.png'\n",
    "plt.savefig(output_path, dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.show()  # Display the pie chart\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env_raptomics",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.11"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "241311488dc32b6bdefbd301f6cadbd3f9f97950c320399a50a7fc022a3a18bf"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
